egttools.numerical¶

The numerical module contains functions and classes to simulate evolutionary dynamics in finite populations.

Functions

stationary_distribution_from_sparse

Compute the stationary distribution of an explicit sparse transition matrix.

Classes

`AGoSResult`	Average gradient of selection G^A(x) from a network MC estimator.
`AbsorptionProbabilityResult`	Per-strategy fixation (absorption) probabilities.
`AbsorptionTimeResult`	Mean absorption (fixation) time from a given initial state.
`FixationResult`	Fixation probability of an invading strategy into a resident population.
`GeneralPopulationEvolver`	Evolver for a general population structure.
`MLSGarcia`	Multi-level selection following Garcia & van den Bergh (2011).
`MLSTraulsen`	Multi-level selection following Traulsen & Nowak (2006).
`NetworkEstimator`	Wraps any `NetworkMCEstimator*` C++ instance and returns rich result objects with optional progress bars.
`PairwiseComparisonEstimator`	Wraps `egttools.numerical.PairwiseComparisonNumerical` and returns rich result objects with standard errors and optional progress bars.
`PairwiseComparisonNumerical`	Numerical solver for evolutionary dynamics under the pairwise comparison rule.
`PairwiseComparisonTransitionOperator`	Matrix-free transition operator for the pairwise comparison process.
`StationaryDistributionResult`	Estimated stationary distribution over population states.
`StationaryIndicatorResult`	Result of `PairwiseComparisonNumerical.estimate_stationary_indicators`.
`StrategyDistributionResult`	Time-averaged strategy frequencies with uncertainty.

`estimators`	Python wrapper classes for egttools MC estimators.
`indicators`	Indicator result types and statistics helpers for PairwiseComparisonNumerical.
`linear_operator`	Matrix-free linear operator wrappers and stationary-distribution solvers.
`mpi`	MPI-distributed stationary distribution solvers backed by PETSc/SLEPc.
`numerical_`	The numerical module contains optimized functions and classes to simulate evolutionary dynamics in large populations.
`results`	Rich result objects returned by egttools MC estimator wrappers.
`structure`	The structure submodule contains population structures